System and method for monitoring online retail platform using artificial intelligence

ABSTRACT

A method and system for monitoring an e-commerce platform. The method includes: receiving, by a computing device, a feedback submitted by a user through the e-commerce platform; generating a vector based on content of the feedback, context of the feedback and profile of the user using AI processors; and classifying the vector to determine function corresponding to the feedback and status of the function using AI classifiers. The content includes text, voice, image and video; the context includes time, location and submission channel of the feedback; the profile includes attributes, history and preference of the user. Dimensions of the vector respectively corresponding to the text, voice, image, video, time, location, submission channel, attributes, history, and preference of the user.

CROSS-REFERENCES

Some references, which may include patents, patent applications andvarious publications, are cited and discussed in the description of thisdisclosure. The citation and/or discussion of such references isprovided merely to clarify the description of the present disclosure andis not an admission that any such reference is “prior art” to thedisclosure described herein. All references cited and discussed in thisspecification are incorporated herein by reference in their entiretiesand to the same extent as if each reference was individuallyincorporated by reference.

FIELD

The present disclosure relates generally to monitoring health status ofan e-commerce platform, and more particularly to system and methods forreal time monitoring health of online retail platforms via deep learningbased on feedbacks from users.

BACKGROUND

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent it is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

E-commerce has undergone a hyper growth for years. Giant online retailplatforms such as Amazon, Alibaba and JD, have hundreds of millions ofactive users and a gross revenue to billions. Moreover, both the revenueand the user number grow rapidly (taking JD as an example, 40% quarterlygrowth of business volume in Q2 2017).

On the one hand, the huge business volume came with massive usage of theonline retail platform (mobile app and website). The massive usageproposes challenges to the availability and stability of the platform.Thus, an unaware breakdown of the platform will be hazardous to userexperience, revenue and public reputation, leading to severe financialand social consequences.

On the other hand, the hyper growth is owed to rapidly increasingfunctions and/or features of the website. On an online retail website,there are hundreds of merchandise product channels, and a series offunctions including search, recommendation, shopping cart, delivery, andpayment. All these functions and channels are developed or owned bydifferent teams inside the company. The functions usually overlap witheach other and more often be upstream or downstream dependency of eachother. This complication creates barrier for each team to know exactlythe health status of its owned function, diagnose errors and providesolutions.

Therefore, an unaddressed need exists in the art to address theaforementioned deficiencies and inadequacies.

SUMMARY

In certain aspects, the present disclosure relates to a method formonitoring health of an e-commerce platform. In certain embodiments, themethod includes: receiving, by a computing device, a feedback submittedby a user through the e-commerce platform; generating, by feedbackprocessors of the computing device, a vector based on content of thefeedback, context of the feedback and profile of the user; andclassifying, by classifiers of the computing device, the vector toobtain a function of the e-commerce platform corresponding to thefeedback and a status of the function, and preparing an alarm when thestatus is malfunction. The content includes at least one of text, voice,image and video; the context includes at least one of time of submittingthe feedback, location of submitting the feedback, and submissionchannel of the feedback; and the user profile includes at least one ofattributes of the user, purchasing history of the user, and preferenceof the user using the e-commerce platform. The vector has apre-determined number of dimensions, and each of the text, the voice,the image, the video, the time of submitting the feedback, the locationof submitting the feedback, the submission channel of the feedback, theattributes of the user, the purchasing history of the user, and thepurchasing preference of the user corresponds to at least one of thedimensions of the vector.

In certain embodiments, the feedback processors and the classifiers areperformed using at least one artificial intelligence model.

In certain embodiments, wherein the step of generating the vectorcomprises: processing the content using the feedback processors toobtain content dimensions of the vector corresponding to the text, thevoice, the image, and the video. In certain embodiments, the methodfurther includes: cleaning the content before processing the content toobtain the content dimensions of the vector. In certain embodiments, themethod further includes: separating the image to text of the image andbackground image, processing the text of the image to obtain an imagetext result and processing the background image to obtain a backgroundimage result, and integrating the image text result and the backgroundimage result to obtain the content dimension of the vector correspondingto the image.

In certain embodiments, the method further includes: sending the alarmto a manager of the e-commerce platform responsible for the function,receiving an instruction corresponding to the alarm from the managerwhen the alarm is false, and re-train the feedback processors and theclassifiers using the instruction.

In certain embodiments, the classifiers are trained using a plurality ofhistorical feedbacks and a function category structure, the functioncategory structure comprises: a tier-1 category comprising website ofthe e-commerce platform, application of the e-commerce platform, andexternal links to the e-commerce platform. In certain embodiments, thetier-1 category of the website comprises tier-2 categories of: productpage, shopping cart, and payment; the tier-2 category of the productpage comprises tier-3 categories of: product description, productsearch, and product recommendation.

In certain embodiments, the classifiers comprise a plurality ofclassification models, each classification model provides a candidatefunction based on each of the historical feedbacks, and the candidatefunctions provided by the classification models are used by an ensemblemodel to determine the function corresponding to each of the feedback

In certain aspects, the present disclosure relates to a system formonitoring health of an e-commerce platform. In certain embodiments, thesystem includes a computing device. The computing device has a processorand a storage device storing computer executable code. The computerexecutable code, when executed at the processor, is configured toperform the method described above.

In certain aspects, the present disclosure relates to a non-transitorycomputer readable medium storing computer executable code. The computerexecutable code, when executed at a processor of a computing device, isconfigured to perform the method as described above.

These and other aspects of the present disclosure will become apparentfrom following description of the preferred embodiment taken inconjunction with the following drawings and their captions, althoughvariations and modifications therein may be affected without departingfrom the spirit and scope of the novel concepts of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate one or more embodiments of thedisclosure and together with the written description, serve to explainthe principles of the disclosure. Wherever possible, the same referencenumbers are used throughout the drawings to refer to the same or likeelements of an embodiment.

FIG. 1 schematically depicts a workflow of an e-commerce platformmonitoring system according to certain embodiments of the presentdisclosure.

FIG. 2 schematically depicts an e-commerce platform monitoring systemaccording to certain embodiments of the present disclosure.

FIG. 3 schematically depicts AI processors according to certainembodiments of the present disclosure.

FIG. 4 schematically depicts an image processing procedure according tocertain embodiments of the present disclosure.

FIG. 5 schematically depicts a feature vector according to certainembodiments of the present disclosure.

FIG. 6 schematically depicts a feature matrix according to certainembodiments of the present disclosure.

FIG. 7 schematically depicts AI classifiers according to certainembodiments of the present disclosure.

FIG. 8 schematically depicts a database according to certain embodimentsof the present disclosure.

FIG. 9 schematically depicts a method for training an e-commerceplatform monitoring system according to certain embodiments of thepresent disclosure.

FIG. 10 schematically depicts structure of functions according tocertain embodiments of the present disclosure.

FIG. 11 schematically depicts an ensemble structure according to certainembodiments of the present disclosure.

FIG. 12 schematically depicts a method to integrate all theone-versus-all classifiers according to certain embodiments of thepresent disclosure.

FIG. 13 schematically depicts a method for using an e-commerce platformmonitoring system according to certain embodiments of the presentdisclosure.

DETAILED DESCRIPTION

The present disclosure is more particularly described in the followingexamples that are intended as illustrative only since numerousmodifications and variations therein will be apparent to those skilledin the art. Various embodiments of the disclosure are now described indetail. Referring to the drawings, like numbers indicate like componentsthroughout the views. As used in the description herein and throughoutthe claims that follow, the meaning of “a”, “an”, and “the” includesplural reference unless the context clearly dictates otherwise. Also, asused in the description herein and throughout the claims that follow,the meaning of “in” includes “in” and “on” unless the context clearlydictates otherwise. Moreover, titles or subtitles may be used in thespecification for the convenience of a reader, which shall have noinfluence on the scope of the present disclosure. Additionally, someterms used in this specification are more specifically defined below.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Certain terms that are used todescribe the disclosure are discussed below, or elsewhere in thespecification, to provide additional guidance to the practitionerregarding the description of the disclosure. It will be appreciated thatsame thing can be said in more than one way. Consequently, alternativelanguage and synonyms may be used for any one or more of the termsdiscussed herein, nor is any special significance to be placed uponwhether or not a term is elaborated or discussed herein. Synonyms forcertain terms are provided. A recital of one or more synonyms does notexclude the use of other synonyms. The use of examples anywhere in thisspecification including examples of any terms discussed herein isillustrative only, and in no way limits the scope and meaning of thedisclosure or of any exemplified term. Likewise, the disclosure is notlimited to various embodiments given in this specification.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure belongs. It willbe further understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art and thepresent disclosure, and will not be interpreted in an idealized oroverly formal sense unless expressly so defined herein.

As used herein, “around”, “about”, “substantially” or “approximately”shall generally mean within 20 percent, preferably within 10 percent,and more preferably within 5 percent of a given value or range.Numerical quantities given herein are approximate, meaning that the term“around”, “about”, “substantially” or “approximately” can be inferred ifnot expressly stated.

As used herein, “plurality” means two or more.

As used herein, the terms “comprising”, “including”, “carrying”,“having”, “containing”, “involving”, and the like are to be understoodto be open-ended, i.e., to mean including but not limited to.

As used herein, the phrase at least one of A, B, and C should beconstrued to mean a logical (A or B or C), using a non-exclusive logicalOR. It should be understood that one or more steps within a method maybe executed in different order (or concurrently) without altering theprinciples of the present disclosure. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items.

As used herein, the term “module” may refer to, be part of, or includean Application Specific Integrated Circuit (ASIC); an electroniccircuit; a combinational logic circuit; a field programmable gate array(FPGA); a processor (shared, dedicated, or group) that executes code;other suitable hardware components that provide the describedfunctionality; or a combination of some or all of the above, such as ina system-on-chip. The term module may include memory (shared, dedicated,or group) that stores code executed by the processor.

The term “code”, as used herein, may include software, firmware, and/ormicrocode, and may refer to programs, routines, functions, classes,and/or objects. The term shared, as used above, means that some or allcode from multiple modules may be executed using a single (shared)processor. In addition, some or all code from multiple modules may bestored by a single (shared) memory. The term group, as used above, meansthat some or all code from a single module may be executed using a groupof processors. In addition, some or all code from a single module may bestored using a group of memories.

The term “interface”, as used herein, generally refers to acommunication tool or means at a point of interaction between componentsfor performing data communication between the components. Generally, aninterface may be applicable at the level of both hardware and software,and may be uni-directional or bi-directional interface. Examples ofphysical hardware interface may include electrical connectors, buses,ports, cables, terminals, and other I/O devices or components. Thecomponents in communication with the interface may be, for example,multiple components or peripheral devices of a computer system.

The present disclosure relates to computer systems. As depicted in thedrawings, computer components may include physical hardware components,which are shown as solid line blocks, and virtual software components,which are shown as dashed line blocks. One of ordinary skill in the artwould appreciate that, unless otherwise indicated, these computercomponents may be implemented in, but not limited to, the forms ofsoftware, firmware or hardware components, or a combination thereof.

The apparatuses, systems and methods described herein may be implementedby one or more computer programs executed by one or more processors. Thecomputer programs include processor-executable instructions that arestored on a non-transitory tangible computer readable medium. Thecomputer programs may also include stored data. Non-limiting examples ofthe non-transitory tangible computer readable medium are nonvolatilememory, magnetic storage, and optical storage.

The present disclosure will now be described more fully hereinafter withreference to the accompanying drawings, in which embodiments of thepresent disclosure are shown. This disclosure may, however, be embodiedin many different forms and should not be construed as limited to theembodiments set forth herein; rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the present disclosure to those skilled in the art.

As described above, there is a need to accurately monitor health of ane-commerce platform. In certain embodiments, a monitor method is to makethe platform running log and set metrics to monitor, i.e. if number ofclicks increased drastically within a short period of time, it may be amalfunction and an alarm is sent. However, the metrics is affected bymany other factors besides the malfunction. For example, the increasingnumber of clicks may be due to business growth or holidays, instead ofmalfunction. Hence, a more straightforward, accurate information sourceto monitor the health status is demanded.

In certain embodiments, user's feedback is regarded as the informationsource. However, the traditional way of manually checking user feedbackis not applicable to giant online retail platform. The platform hashundreds or even thousands of functions, and thus it is hard for peopleto remember all the definition and subtle difference, not to mentiongiving accurate response. Moreover, the latency is high since peopleneed time to understand the feedback, check references and respond.Last, the cost is high to maintain a huge team.

In certain aspects, the present disclosure provides a self-sufficiency,self-refining system, to monitor health status of online retailplatform. By utilizing user's feedback and integrating them withknowledges about the platform, the system is able to detect functionissues in a timely, accurate and automatic manner, via harnessing thepower of artificial intelligence (AI) including natural languageprocessing, computer vision, and machine learning.

In details, the system established a knowledge base about the e-commercecompanies' team structure, app function structure, and thecorrespondence between these two. Meanwhile, the system instantly parseuser feedback, in various format (texts, voice, images, video), andextract useful information from them via AI technologies. Finally, thesystem combine the two information source to make judgement—send alarmsto particular team to fix reported issues. The information extractedfrom feedbacks includes: which function is unhealthy, and which actionshould be taken to fix it. The actions include: find the team who ownsthis function, notify the team it is problematic, and give the reasonand suggest actions.

In certain embodiment, the disclosure related to a software system thatembeds AI technologies to enable instant, accurate health monitoring ofonline retail platform based on user feedback. The various format offeedback entails the uniqueness of how AI technologies are usedhere—there is a big variety of inputs (structured data, unstructureddata, text, image audio, video) and thus the system has intensiveensemble methods to integrate all the different format of data, tocreate the most accurate issue reports.

FIG. 1 schematically shows a global workflow of the software systemaccording to certain embodiments of the present disclosure. As shown inFIG. 1, the user 102 performs an activity 104 of submitting a feedback106. The submitted feedback has multimedia content 1060, including text1062, audio or voice 10764, image 1066, and video 1068. The systemrecords context 1040 of the submission, including time 1042 ofsubmitting the feedback, location 1044 where the user submits thefeedback, and submission channel 1046 of the submission. The submissionchannel includes application (APP) or website. The submission is relatedto a user identification (ID), and the system can access user profile1020 based on the user ID identified, includes attributes 1022, history1024, and preference 1026. The attributes 1022 includes registeredinformation of the user, such as gender, age, hobby, mail address. Thehistory 1024 includes the purchase history of the user and optionallyfeedback history of the user or other activities of the user using thee-commerce platform. The preference 1026 includes the user's preferenceusing the website, such as product he is interested in based on hissearch history. Content data are first processed via AI processors 108and transferred to structured data. The data pool 112 stores thestructured data from the AI processors 108, the context data 1040 ofsubmissions, and the profile data 1020 of the users, and is configuredto integrates the structured data from the AI processors 108, thecontext 1040 related to the feedback submission, and the profile 1020related to the feedback submission to form a vector or a matrix of oneor more feedbacks. When the system is trained, a matrix is generatedbased on many feedbacks. When the system is used for monitoring, avector may be generated for each feedback, and the feedback is thenanalyzed to determine its function and health status. After generationor integration of the vector or matrix, the profile 1020 then sends thevector or matrix to the AI classifiers 114. The machine learningclassifiers 114 are applied to predict whether the feedback(s) 106 isfunction related at step 116, if it's function related, to predictwhether the function is healthy at step 118, and to predict whichfunction has problem at step 120. The system then match the responsibleowner or manager of the function based on the function owner knowledges132 and sends a malfunction report or alarm 122 to the responsible owner124 of that function. When the function owner determines at step 126that the alarm is true, he fixes the malfunction at step 128. If thealarm is false, the function owner sends the false alarm to the database130, and provide the false alarm to the function owner knowledge 132, soas to use the updated function owner knowledge to retrain the AIclassifiers 114. The data pool 112 and the database 120 may be oneintegrally formed database, which includes the context 1040 of thehistorical feedbacks, the profile 1020 of the platform users, the matrixand vectors generated by the AI processor 108, the function labels ofthe feedbacks (either manually added or generated during training of theAIs), etc.

During the process, AI technologies are used for processing raw contentand make decisions. In terms of AI, the data acquisition isindispensable to the success of the AI models. In certain embodiments,the system stores massive amount of historical data in the database totrain the AI models. Further, online training mechanism is enabled—onceAI made a mistake, e.g., send a false alarm, the mistake informationwill be immediately sent back AI models for an online retrain.Accordingly, the system is real-time self-refined.

FIG. 2 schematically depicts a health monitoring system for ane-commerce platform according to certain embodiments of the presentdisclosure. The system 200 shown in FIG. 2 and the system 100 shown inFIG. 1 are different ways of showing the same or similar system. Asshown in FIG. 2, the system 200 includes a server computing device 210,multiple managing computing devices 250, and a network 240 connectingthe managing computing devices 250 with the server computing device 210.

The server computing device 210 may function as a server or a hostcomputer. In certain embodiments, the server computing device 210 may bea cloud computer, a server computer, a cluster, a general-purposecomputer, or a specialized computer, which provides platform monitoringservice. In certain embodiments, the managing computing devices 250 maybe cloud computers, mobile devices, tablets, general-purpose computers,headless computers, wearable devices, or specialized computers, whichreceive alarms form the server computing device 210 and in response tothe alarm, sends evaluation of the alarm to the server computing device210. In certain embodiments, the network 240 may be a wired or wirelessnetwork, and may be of various forms, such as a public network and aprivate network. Examples of the network may include, but not limitedto, the LAN or a wide area network (WAN) including the Internet. Incertain embodiments, two or more different networks and/or interfacesmay be applied to connect the server computing device 210 to the usercomputing devices 250. In certain embodiment, the interface 240 may alsobe a system interface, a universal serial bus (USB) interface.

As shown in FIG. 2, the server computing device 210 may include, withoutbeing limited to, a processor 212, a memory 214, and a storage device216. In certain embodiments, the server computing device 210 may includeother hardware components and software components (not shown) to performits corresponding tasks. Examples of these hardware and softwarecomponents may include, but not limited to, other required memory,interfaces, buses, Input/Output (I/O) modules or devices, networkinterfaces, and peripheral devices.

The processor 212 may be a central processing unit (CPU) which isconfigured to control operation of the server computing device 210. Theprocessor 212 can execute an operating system (OS) or other applicationsof the server computing device 210. In some embodiments, the servercomputing device 210 may have more than one CPU as the processor, suchas two CPUs, four CPUs, eight CPUs, or any suitable number of CPUs. Thememory 214 can be a volatile memory, such as the random-access memory(RAM), for storing the data and information during the operation of theserver computing device 210. In certain embodiments, the memory 214 maybe a volatile memory array. In certain embodiments, the server computingdevice 210 may run on more than one memory 214. The storage device 216is a non-volatile data storage media for storing the OS (not shown) andother applications of the server computing device 210. Examples of thestorage device 216 may include non-volatile memory such as flash memory,memory cards, USB drives, hard drives, floppy disks, optical drives,solid-state drive (SSD), or any other types of data storage devices. Incertain embodiments, the storage device 216 may be a local storage, aremote storage, or a cloud storage. In certain embodiments, the servercomputing device 210 may have multiple storage devices 216, which may beidentical storage devices or different types of storage devices, and theapplications of the server computing device 210 may be stored in one ormore of the storage devices 216 of the computing device 210. As shown inFIG. 2, the storage device 216 includes a platform monitor 220. Theplatform monitor 220 provides a service for monitoring an e-commerceplatform using feedbacks from users or customers.

The platform monitor 220 includes, among other things, a feedbackextraction module 222, AI processors 224, a vector generator 226, an AIclassifier 228, a manager communication module 230, and a database 232.In certain embodiments, the platform monitor 220 may include otherapplications or modules necessary for the operation of the modules222-232. It should be noted that the modules are each implemented bycomputer executable codes or instructions, or data table or databases,which collectively forms one application. In certain embodiments, eachof the modules may further include sub-modules. Alternatively, some ofthe modules may be combined as one stack. In other embodiments, certainmodules may be implemented as a circuit instead of executable code. Incertain embodiments, some or all of the modules of the platform monitor220 may be located at a remote computing device or a cloud device.

The feedback extraction module 222 is configured to, retrieve or receivea feedback submitted by a user of an e-commerce platform, extractcontent from the feedback, and send the extracted content to the AIprocessor 224. The content of the feedback includes at least one oftext, audio or voice, image, and video.

In certain embodiments, during training of the platform monitor 220, thefeedback extraction module 222 is configured to retrieve and extracthistorical feedbacks stored in the database 232. To ensure efficienttraining of the platform monitor 220, the feedback extraction module 222may provide only high-quality historical user feedbacks. Those rawtexts, images, audio, video are collected from everyday operation, andall the data are stored in internal database and ready for use intraining AI algorithms.

In certain embodiments, during operation of the platform monitor 220,new feedbacks are added to the database 232, and the platform monitor220 is configured to check the database 232 in a short pre-determinedtime interval, and process the newly added feedbacks as a batch.

In certain embodiments, the platform 220 checks feedbacks at real time,and the feedback extraction module 222 is configured to receive onefeedback at a time and send the extracted content of the one feedback tothe AI processor 224.

The AI processor 224 is configured to, upon receiving the content fromthe feedback extraction module 222, transform the content into structurecontent data, such as a content vector or a content matrix. Referring toFIG. 3, the AI processor 224 includes a content pre-processor 2240, atext processing module 2242, a speech recognition module 2244, an imageprocessing module 2246, and a video processing module 2248.

The content pre-processor 2240 is configured to remove noise from thecontent, so as to provide cleaned data to the text processing module2242, the speech recognition module 2244, the image processing module2246 and the video processing module 2248. The text processing module2242 is configured to, upon receiving the cleaned text, transform thetext into numeric values. The speech recognition module 2244 isconfigured to, upon receiving the cleaned audio, recognize text from theaudio, and transform the recognized text into numeric values. The imageprocessing module 2246 is configured to, upon receiving cleanedimage(s), separate the image into text and background image (imageportion with text removed from the image), respectively process the textand background image, and integrate the results to numeric values. Thevideo processing module 2248 is configured to, upon receiving thecleaned vide, separate the images of the video into text and backgroundimage, process respectively the texts and the background images, andintegrate them to obtain numeric values. Those values from processingthe content, are defined as dimensions of the content vector whenprocessing one feedback, and are defined as dimensions of the contentmatrix when processing multiple feedbacks (such as during training).

In certain embodiments, the text processing module 2242 is configuredto, upon receiving the cleaned text from the content-processor 2240,obtain features or numeric values for the text. Specifically, the textprocessing module 2242 first partitions each text as a sequence ofwords, and then featurizes the words as dimensions of the contentvector, representing word occurrence, word co-occurrence, word class,name entity, sentence syntactic structure and words' semantic meaning[8]. Many technologies may be used: for example, n-gram and tfidf areused to denote word occurrence [8], word2vec [9] is used to representword by its contexts (co-occurrence with other words) [8], POS and nameentity recognition is used to find word class and name entity of theword, syntactic analysis and semantic analysis are further applied toget the word's syntactic role and semantic meaning [8]. During training,the featurization leads to a big matrix, with columns as theabove-mentioned features and rows as feedbacks. The processing ofmultiple texts is used for training the model or using the model. Duringoperation, when the application processes new feedbacks as batch, thefeaturization of the batch of feedbacks also leads to a matrix. Duringoperation, when the application processes new feedbacks one by one, thetext processing module 2242 may also process the one feedback to resultdimensions of a vector.

In certain embodiments, the image processing module 2246 is configuredto, upon receiving image(s) from the content pre-processor 2240 (or fromthe feedback extraction module 222 if no data cleaning is needed at thisstage), obtain features or numerical values of the image. Specifically,the image processing module 2246 separates the image into text extractedfrom the image (image text) and background image, processes the imagetext and the background image separately, and integrate the two toobtain numerical values. In certain embodiments, the image processmodule 2246 applies techniques based convolutional neural network [10]or deep neural network, to represent the background images as meaningfulcontent vector dimensions.

FIG. 4 schematically shows an example of obtaining content vectordimensions of an image. As shown in FIG. 4, a screenshot 402 of a phonescreen is provided in a feedback. First, the image processing module2246 separates the original image 402 into two parts, the image text of“What are you looking for” and the background image 404 with the imagetext removed. The extraction of the image text may be performed usingOCR method trained on internal data. Using the extracted text box andtheir coordinates in the image, the text box is extracted out, and thebackground image is obtained. Then the image processing module 2246processes the image text to find keywords “looking for” and thesyntactic structure “What are . . . ?” In certain embodiments, thekeywords is selected by tfidf+ngram, while syntactic structure isselected by matching the image text to predefined domain-specificstructures. In certain embodiments, to predefine sentence structures,all image texts extracted from images in training data are collected.Then the image texts are cut into sentences. Taking each pair ofsentences, a similarity score between the two sentences are calculatedin terms of the words in them. Specifically, taking each sentence as aset of words S={w}, the similarity between sentence i and sentence j isdefined as:

s(i,j)=|S _(i) ∩S _(j)|

Using this similarity score, a clustering method, K-means is used toseparate the sentences in training data into M groups: G₁, . . . ,G_(M). M is pre-defined based on estimated number of syntacticstructures in the text corpus. Each group has sentences with similarword vector. For a new sentence k, its closest group is calculatedusing:

G(k)=argmin_(G1, . . . , GM)(Σ_(i∈Gm) s(k,i))

Following similar procedures, background images are featured intovectors based on their image representation via, for example,AutoEncoder method. Then similarity between background images aredefined as cosine similarity and the background images are separatedinto groups. For a new image, its closest image group can be obtained.As shown in FIG. 4, a possibility of groups are obtained based on eitherimage text or background image, and the two results can be integrated toobtain a more accurate estimation of the group or related function ofthe original image. In certain embodiments, the OCR texts' andbackground images' group ID are used as additional features,concatenated after the text dimension(s) of the content vector orcontent matrix. As the result shown in FIG. 4, the result from theperformance of the image processing module 2246 gives the search page a96% possibility, which may form a corresponding dimension value 1 of thecontent vector or content matrix corresponding to the group ID of“search page.”

Audio can be recognized to obtain text, and video can be separated intoimages, and the processing of the audio and the video is similar to theprocedure described above in related to the text and the image.

The vector generator 226 is configured to, upon receiving the contentvector or content matrix from the AI processor 224, retrieve the numericvalue(s) of the context and the numeric value(s) of the user profile,append the values to the content vector or content matrix, to form avector or matrix. In certain embodiments, the context and the profileare stored in the database 232. In certain embodiments, if the numericvalues of the context and the user profile are not available in thedatabase 232, the vector generator 226 is further configured totransform the context and user profile into numerical values. In certainembodiments, the transformation is performed using dummy variables. Forexample, if there are 100 cities for the location 1044 of a submission,100 dummy variables are provided to denote them, respectively. If thecity is “Beijing” (the first city, for instance), then the first dummyvariable is set to 1 while the other dummy variables are set to 0. Incertain embodiments, the vector generator 226 may also be a part of theAI processors 224.

FIG. 5 schematically shows a vector or (or named feature vector) of afeedback generated by the vector generator 226 according to certainembodiments of the present disclosure. As shown in FIG. 5, the vectorincludes features from the text, the image, the context of thesubmission and the user profile. The image features includes image textfeature and the background image feature. In certain embodiments, thevector include a pre-determined number of dimensions. As shown in FIG.5, the vector includes m+n+p+q+8 dimensions, wherein each of m, n, p andq are positive integers. In certain embodiments, the vector includesabout 5,000 dimensions, and most of the dimensions correspond to wordsor phrases from the text 1062. The feature dimensions shown in FIG. 5 isfor description only and may be varied during operation. For example,the time may be divided into dimensions of season, month, weekday, hourof the day, and each of the dimensions are defined using a dummyvariable. In certain embodiments, each of the location, submissionchannel, attributes is defined by a dummy variable. In certainembodiments, the history of the user refers to the complain history andthe history of how active the user uses the website, and the history isdefined by one or more real numbers. After the vector is generated, thefeature vector generator 226 sends the vector to the AI classifier 228to make decisions—notify the malfunction.

During training or batch processing, the vector generator 226 isconfigured to generate a matrix (or named feature matrix) instead of avector. FIG. 6 schematically show a matrix of feedbacks according tocertain embodiments of the disclosure. As shown in FIG. 6, each row ofthe matrix is a vector corresponding to one feedback. Each row includestext features (word feature, syntactic feature, semantic feature), imagetext feature, background image feature, context features, and userprofile features, those features are represented by values, and thevalues in one row are dimensions of the vector corresponding to thatfeedback. In certain embodiments, a feedback in a row may not havecorresponding features for each dimension, and the dimensions thefeedback lacks are given a value 0. Kindly note each row of the matrixshown in FIG. 6 includes similar information as the vector shown in FIG.5.

The AI classifier 228 is configured to, upon receiving the vector/matrixfrom the vector generator 226, determine whether the feedback relates toa function of the e-commerce platform, the status of the function, andwhich function. Referring to FIG. 7, the AI classifier 228 includes afunction determination module 2280, a function status module 2282, and areport module 2284. The function determination module 2280 is configuredto use various classification models to process the vector/matrix, toobtain one or more functions related to the vector (or each row of thematrix), and send the function(s) to the function status module 2282.Typically, the function determination module 2280 only selects onefunction as the result for each vector. The function status module 2282is configured to, upon receiving the determined function, evaluatewhether the function is healthy or not based on the vector. Whenmalfunction presents, the function status module 2282 is configured tosend the malfunction result to the report module 2284. In certainembodiments, the function status module 2282 may also determines thatthe status of the function is not related to the function of theoperation of the platform, but related to a product itself, it may thensend the result to the product department to take care of the feedbackaccordingly.

The report module 2284 is configured to, upon receiving the malfunctionstatus from the function status module 2282, retrieve the functiondetermined by the function determination module 2280, and send thefunction and the status of the function to the manager communicator 230.In certain embodiments, the function status module 2282 may send thefunction and the status of the function directly to the managercommunicator 230, and a separate report module 2284 is not needed.

The manager communicator 230 is configured to, upon receiving thefunction and the status of the malfunction, match the function to themanager based on the manager-function relationship (or function ownerknowledge) stored in the database 232, prepare an alarm based on thedetermined function and the status of the malfunction, match thefunction to the manager of the function, and send the alarm to themanager. The manager of the function, upon receiving the alarm, fix themalfunction when it's his responsibility. If the malfunction is notrelated to the manager or not the responsibility of the manager, themanager would send back a response to the manager communicator 228. Themanager communicator 228 then stores the response to the database 232.The updated database 232 can then be used to retrain the AI processors224 and the AI classifiers 228. In certain embodiments, the responseform the manager may also be stored directly to the database 232.

The database 232 includes data for training and using the AI processor224 and the AI classifier 228. Referring to FIG. 8, the database 232includes feedback content 2320, feedback context 2322, user profile2324, feedback vectors 2326, feedback functions 2328, feedback status2330, function-manager list 2332, manager response 2334, and feedbackfix 2334. The feedback content 2320 includes high quality historicalfeedbacks from users and new feedbacks to be processed by the platformmonitor 220. The feedback context 2322 stores the context of submittingthe feedbacks. The user profile 2324 includes profiles of the users,which may include all the users registered to the e-commerce website, oronly the users that have submitted feedbacks. The feedback vectors 2326stores the feedback vectors of the historical feedbacks, which can beused for training the AI processors 224. The feedback functions 2328includes groups of functions corresponding to the historical feedbacks.The feedback status 2330 includes a group of status corresponding to thehistorical feedbacks. The function-manager list 2332 lists thecorrespondence between the functions and the managers responsible forthe functions. The manager response 2334 includes responses from themanagers about false alarms generated by the manager communicator 230.The malfunction fix 2336 stores the method of solving a malfunction,when available. The data in the database are indexed by identifications,such as user ID registered by the user or session ID of activities, andthe data can be retrieved using those identifications.

In certain embodiments, the databased 232 includes data for the trainingof the platform monitor 220. In certain embodiments, the database 232also includes data that are used during the operation of the platformmonitor 220. In certain embodiments, the database 232 may not includeall the above listed components, and some data listed in the database232 may be stored in other servers or computing devices, and areaccessible by the platform monitor 220 during operation. For example,the function-manager list 2332 may be stored in another device that isaccessible by the manager communicator 230; and the feedback fix 2336may be stored by the respective mangers. In certain embodiments, thedatabase 232 may include other documents necessary for the operation ofthe platform monitor 220. In certain embodiments, once a new feedback isanalyzed, the corresponding feature vector generated by the AI processor224, the function and status determined by the AI classifier 228, andoptionally the manager response are stored in the database 232 to updatethe database 232. In certain embodiments, the AI processors 224 and theAI classifiers 228 are retrained regularly or retrained every time afalse alarm is generated.

FIG. 9 schematically shows a method of training the AI processors andthe AI classifiers according to certain embodiments of the presentdisclosure. In certain embodiments, the method is implemented by theserver computing device 210 shown in FIG. 2. In certain embodiments, theAI processors and the AI classifier are trained independently, where theoutput of the AI processor, the feature vectors of historical feedbacks,are used as input for the AI classifier. It should be particularly notedthat, unless otherwise stated in the present disclosure, the steps ofthe method may be arranged in a different sequential order, and are thusnot limited to the sequential order as shown in FIG. 9.

At procedure 902, high-quality historical user feedbacks are provided.Raw texts, images, audio, video are collected from every day operationof the e-commerce platform. All the data are stored in the internaldatabase, such as the database 232 and ready for use in training AIalgorithms. In certain embodiments, the procedure 902 is performed bythe feedback extraction module 222.

At procedure 904, the raw feedback data are cleaned to remove noises. Incertain embodiments, the procedure 904 is performed using the contentpre-processor 2240 or any other independent modules, which may be an AImodel. The raw texts, images, audio, and video are noisy, and some ofthe data are not related to malfunctions. For example, users typednon-informative characters, uploaded nonsense or low-qualityimages/videos. In certain embodiments, one or more AI models are trainedto recognize the noisy patterns and remove the noises accordingly. Inone embodiment, natural language processing is used to match noisy textsand remove them. In one embodiment, images having complicated, noisybackground are removed, because such images are usually not screenshotswhich reflect app/website errors.

Further, the key information usually is only a small part of raw contentand thus information extraction is critical. Video is divided into audioand images. Audio is transferred to texts [4]. Texts in images areextracted [5] and image backgrounds are left. This is due to the factthat most users submit images as screenshots when the app breakdown.

After that, the cleaned images and texts are stored in database alongwith context features such as timestamp, location of feedback submissionand submission channel, and user profile features. They together formthe training data of the platform monitor 220, specifically the AIprocessors 224 and the AI classifiers 228.

At procedure 906, the data are labeled. In addition to feedback, labelof the feedback is also indispensable to success of AI [6]. The labelestablishes the connection between feedback and their usage—themalfunction of the online retail platform.

Referring back to FIG. 1, we need to map malfunctions with particularteams. Hundreds of categories are defined, each denotes one uniquefunction issue. The quality of label impacts the accuracy of AI trainedon it, so it has to be of high quality. In certain embodiments, thelabel are obtained from historical platform malfunction reports—from agroup of professionals with years of experiences on manually labelinguser feedbacks. In certain embodiments, the functions of an onlineretail platform is defined with a tree structure, with several tier-1function module divided into multiple tier-2 modules, and each tier-2module further divided into multiple tier-3 modules, and so on and soforth. There are hundreds of modules serving like leaves of the tree.With the labeling structure and training, the AI can learn how to labelan incoming feedback to one of the leaves.

In certain embodiments, AI processors are fixed for a feedback, andparameters of the AI classifiers are adjusted to refine the AIclassifier according to the quality of the result, where the result maybe the percentage of the correct alarm generated by the AI classifiers;in other embodiments, AI classifiers are fixed, and parameters of the AIprocessors are adjusted to refine the AI processor according to thequality of the result, where the result may be the percentage of thecorrect alarm generated by the AI classifiers. In certain embodiments,the above method can also be used to select a suitable AI model for apart of the platform monitor 220, that is. In other words, by fixing theAI classifiers and varying one of the AI processors, a suitable AIprocessor model can be selected; and by fixing the AI processors andvarying one of the AI classifiers, a suitable AI classifier model can beselected. FIG. 10 schematically shows a structure of functions tomonitor according to certain embodiments of the present disclosure. Asshown in FIG. 10, a function structure of the platform 1000 beingmonitored includes three tires. The platform 1000 may be an e-commerceplatform. Tier-1 modules of the platform 1000 include the website 1010of the e-commerce platform, the App 1030 such as a smart phoneapplication, and the external 1050 such as a third party service incommunication with the e-commerce platform. The tier-1 modules here aresubmission channels for the feedbacks of the platform. The tier-1module, website 1010 includes three tier-2 modules—the product page1012, the shopping cart 1014, and the payment 1016. The three tier-2modules of the website 1010 may be visited sequentially by a user andshown in different web pages. For example, the user may browse theproduct page 1012 and find the product he is interested in, add theproduct to the shopping cart through the shopping cart function 1014,review the shopping cart, and make payment through the payment function1016. The tier-2 module product page 1012 further includes four tier-3modules, which includes product description 1012A, product search 1012B,the product recommendation module 1012C, and other related modules. Thethree tier-3 functions are related to the product directly, and may beshown in the same web page.

In certain embodiments, the label categories are manually defined, andthus it is not perfect. There are categories not defined yet and newcategories keep coming out—when new functions are developed for theplatform.

For the former, we collect feedbacks not classified to any ofpre-defined categories as “unknown feedbacks”. There are usuallymultiple unknown categories, so we have to further partition unknownfeedbacks into subgroups via unsupervised machine learning [6]. Extractthe topic information using topic modeling (natural language processingtechniques [7]), and have human intervention to define those undefinedcategories.

For the latter, we followed similar procedure. The only difference isthat new categories usually do not have many feedbacks. So theirfeedbacks are “left overs” after the former step—these feedbacks are notassociated with any defined categories. Finally, we map newly launchedfunctions and match them with the those left overs.

After the data are labeled, at procedures 908-912, the cleaned rawcontents of the feedbacks are transformed to numeric values. The values,together with the context features and user profile features, areintegrated to form a matrix (feedback vectors) corresponding to thefeedbacks. Those procedures 908-912 are performed to train the AIprocessors 224.

At procedure 908, the text processing module 2242 receives texts of thefeedbacks, and in response, processes the texts to obtain features ornumeric values for those texts. In certain embodiments, the textprocessing module 2242 splits each text as a sequence of words, and thentransforms each word as numbers, representing word occurrence, wordco-occurrence, word class, name entity, sentence syntactic structure andwords' semantic meaning. Those numbers are respectively dimensions of afeature vector for each feedback. In certain embodiments, thefeaturization of those words of multiple feedbacks leads to a bigmatrix, with columns as the above-mentioned features and rows asfeedbacks. The matrix is called text matrix.

At procedure 910, the image processing module 2246 receives images ofthe feedbacks, and transform the images to numerical values.Specifically, the image processing module 2246 separates the image intotext extracted from the image (image text) and background image (imagewithout the text), processes the image text and the background imageseparately, and integrate the results to obtain numeric values, and addthe values as new dimensions of the text matrix. In certain embodiments,the audios and videos are transformed to texts and images, and processedsimilarly to obtain their respective values. Those values are added tothe text matrix as new dimensions of the vector, where each rowcorresponding to one feedback and is regarded as the vector for thatfeedback. In certain embodiments, when the feedback only includes text,the procedure 908 is sufficient and the procedure 910 is not necessary.

At procedure 912, the vector generator 226 extract information from thecontext and the user profile of the feedbacks, transform the informationto values, and add the values to the text matrix, to form a matrix ofthe feedbacks (or named feature matrix). Referring back to FIG. 6, amatrix of the feedbacks according to certain embodiments of thedisclosure is shown. The rows of the matrix are vectors of thefeedbacks. Each row (the vector for each feedback) includes textfeatures (word feature, syntactic feature, semantic feature), image textfeature, background image feature, context features, and user profilefeatures, those features are represented by values, and the values inone row are dimensions of the vector corresponding to that feedback. Incertain embodiments, a feedback in a row may not have correspondingfeatures for each dimension, and the dimensions the feedback lacks aregiven a value 0.

Certain dimensions of the feature matrix are obtained by running AIprocessors, and after obtaining the matrix of the feedbacks, atprocedure 914, the matrix and the corresponding function labels (ormalfunction labels) are used as input to train the AI classifiers 228.FIG. 11 schematically shows an ensemble structure according to certainembodiments of the disclosure. Referring to FIG. 11, various machinelearning classification models are applied, followed by ensemblemechanisms [6].

Given that feedback label distribution is imbalanced (some categorieshas far less feedbacks than others), feedback data is resampled viabootstrap [6], to make label more evenly distributed.

Gradient boosting tree classifier [11] is applied as the majorclassifier (e.g. the classification model 1 in FIG. 11). The data is acombination of categorical and continuous value types, and it isimbalanced. Gradient boosting tree is known to be successful with thedata characteristics [11]. Moreover, to reduce bias by using a singlemodel, hierarchical ensemble mechanism is applied. This ensemble is tosynthesize gradient boosting tree with its peer models, such as randomforest, logistic regression with penalty terms [6]. Each classifieroutput a predicted label. The classifiers are named as tier-1classifiers, including classification model 1, classification model 2, .. . , and classification model N. Each of the classification modelscorresponds to a function of the platform. When the feature matrixtier-1 is used as input for each of the models, each model gives aresult of whether the function corresponding to the model is talkedabout in the feedback. The results from all the classification models 1to N are combined to form the feature matrix tier 2. For example, eachresult from one of the classification models is in a form of a binarydecision, and the binary decisions from the classification models 1 to Nare respectively defined as dimensions of a vector. Here the vector isthe feature matrix tier-2 when one feedback is processed, or is one rowof the feature matrix tier-2 when multiple feedbacks are processed. Thefeature matrix tier-2 information are integrated in the ensembleclassifier, and the ensemble classifier provides the result of whetherthe ith label is a function that is related to the feedback or isn'trelated to the feedback. In certain embodiments, the output of whetherthe ith label is related to the feedback or not is in a form of a binarydecision.

One uniqueness of this step is that the tier-1 contexts and the userfeatures are re-used in tier-2. This is because during the training, itis observed that although these features have strong signal but theirnumber is much less than text/image features. So when training tier-1classifier, their signal is buried in the large number of text/imagefeatures. After tier-1, text/image features are reduced to a classlabel, so in tier-2 classifier, there are not many text/image features,and if we put context/user features as input in tier-2, we could makefull use of their power. In certain embodiments, another gradientboosting tree classifier is used as the ensemble classifier.

The process shown in FIG. 11 is to predict individual label. In otherwords, it determines the relationship between the matrix (or vectors inthe matrix) with one specific function. The process can be repeated foreach of the labels or functions. After that, as shown in FIG. 12, theresult for all these labels are integrated to make final decision whichlabel to be given to the feedback. In other words, when whether afeedback is related to each of the functions is determined, the processin FIG. 12 finds one function from the plurality of functions, where theone function relates to or is talked about in the feedback.

In certain embodiments, one-versus-all method [6] is used to trainmodels one by one. This is because there are hundreds of categories—ifwe use one model for all the categories, the optimization will havehundreds of variables and computer can hardly find the optimal model.The one-versus-all means we take one category as positive while allothers as negative. In other words, we train hundreds of 2-classclassifiers, each predict if a feedback belongs to a category or not.Since all these classifiers are trained individually, they may havedisagreement with each other—multiple classifiers may think a feedbackbelongs to their corresponding category but actually the feedback mayonly belong to one of them. So there is a demand to integrate thesehundreds of opinions and find a way to get best consensus based on them.FIG. 12 schematically shows a method to integrate all the one-versus-allclassifiers according to certain embodiments of the disclosure. Notethat it is similar to FIG. 11 but serves distinct purposes. FIG. 11 isto predict individual label, while FIG. 12 is to integrate all theselabels to make final decision which label to be given to the feedback.The label represents a particular malfunction owned by a particularteam, so the label is used to map to a particular team and send the teamthe malfunction notification.

In certain embodiments, the above-mentioned machine learning model isre-trained and updated regularly to learn the latest classificationpatterns in data. In certain embodiments, an online training mechanismis enabled. Once AI made a mistake, e.g., sending a false alarm, themistake information will be immediately sent back to AI models for anonline retrain. So the system is real-time self-refined. Classificationresult is sent over to function owners, which include developers,product specialists and analysts, so that the can take certain actionsto fix the detected issue.

After the training of the AI processors 224 and the AI classifiers 228,the platform monitor 220 can be used to monitor the health of thee-commerce platform. FIG. 13 schematically shows a method of using theplatform monitor 220 to check the health of the e-commerce platformbased on a feedback from a user. In certain embodiments, the method isimplemented by the server computing device 210 shown in FIG. 2. Itshould be particularly noted that, unless otherwise stated in thepresent disclosure, the steps of the method may be arranged in adifferent sequential order, and are thus not limited to the sequentialorder as shown in FIG. 13.

As shown in FIG. 13, at procedure 1302, the feedback extraction module222 extracts content of a feedback. The content may include at least oneof a text, a voice, an image and a video.

At procedure 1304, the content pre-processor 2240 clean the content ofthe feedback.

After the content of the feedback is cleaned, at procedure 1306, the AIprocessors 224 process the cleaned content to obtain a text vector, andsend the feature vector to the vector generator 226. The dimensions ofthe text vector are values correspond to the text in the content, theimage text and background image in the content.

Then at procedure 1308, the vector generator 226 add the context anduser profile of the feedback to the text vector as new dimensions toform a feature vector, and send the feature vector to the AI classifiers228. The new dimensions include the time of the submission, the locationof the submission, the submission channel, the attribute of the user,the history of the user and the preference of the user.

At procedure 1310, in response to receiving the feature vector, theclassifier 228 process the feature vector to obtain a malfunctioncorresponding to the feature vector, and send the malfunctioninformation to the manager communicator 230. Specifically, theclassifier 228 determines whether the feature vector is functionrelated, whether the status of the function represented by the featurevector is normal or abnormal/malfunction, which function is representedby the feature vector.

At procedure 1312, in response to receiving the malfunction information,the manager communicator 230 matches the malfunction to a specificmanager or a team responsible for the malfunction, prepare an alarm, andsend the alarm to the manager.

After receiving the alarm, the manager determines whether themalfunction is the one he is responsible for. If so, he would fix themalfunction. If not, the manager would send a response to the managercommunicator 230 of the platform monitor 220.

At procedure 1314, the manager communicator 230 receives the responsefrom the manager, the response includes the information that thefeedback or the malfunction is not the responsibility of the manager.

At procedure 1316, the manager communicator 230 stores the errorinformation from the manager to the database 232, and using the updateddatabase 232 to retrain the AI classifiers 228.

In summary, the platform monitor according to certain embodiments of thepresent disclosure is a self-sufficiency, self-refining system. Byutilizing the user's feedback (content, context and profile) andintegrating them with knowledges about the platform (company teamstructure, application function structure, and their correspondence),the system is able to detect function issues in a timely, accurate andautomatic manner, via harnessing the power of artificial intelligenceincluding natural language processing, computer vision and machinelearning.

The content, context and profile of a feedback is converted to a vectorhaving a great number of dimensions, which makes the final malfunctiondecision accurate.

The number of dimensions of the vectors are easily expandable, and thefunction category structure is easily expandable, so that theincorporation of newly added information or function is convenient.

In the training of the AIs, the context and profile of the feedback areused under both tier-1 and tier-2 function models, such that the effectof the context and profile information are efficiently considered,without being overwhelmed by the effect of the content of the feedback.

The foregoing description of the exemplary embodiments of the disclosurehas been presented only for the purposes of illustration and descriptionand is not intended to be exhaustive or to limit the disclosure to theprecise forms disclosed. Many modifications and variations are possiblein light of the above teaching.

The embodiments were chosen and described in order to explain theprinciples of the disclosure and their practical application so as toenable others skilled in the art to utilize the disclosure and variousembodiments and with various modifications as are suited to theparticular use contemplated. Alternative embodiments will becomeapparent to those skilled in the art to which the present disclosurepertains without departing from its spirit and scope. Accordingly, thescope of the present disclosure is defined by the appended claims ratherthan the foregoing description and the exemplary embodiments describedtherein.

REFERENCES

-   1. Tomas Mikolov, Ilya Sutskever et al, Distributed Representations    of Words and Phrases and their Compositionality-   2. Quoc Le, Tomas Mikolov, Distributed Representations of Sentences    and Documents-   3. Yoon Kim, Convolutional Neural Networks for Sentence    Classification-   4. Pieraccini, Roberto. The Voice in the Machine. Building Computers    That Understand Speech. The MIT Press-   5. https://github.com/tesseract-ocr/-   6. Trevor Hastie, Robert Tibshirani, and Jerome H. Friedman, The    elements of statistical learning, 2001, Springer-   7. Blei, David, Probabilistic Topic Models, Communications of the    ACM, 2012, 55 (4): 77-84.-   8. Christopher Manning, Hinrich Schutze, Foundations of statistical    natural language processing, 1999, The MIT Press.-   9. Mikolov, Tomas; et al. Efficient Estimation of Word    Representations in Vector Space, arXiv:1301.3781-   10. LeCun, Yann. LeNet-5, convolutional neural networks. Retrieved    16 Nov. 2013.-   11. https://github.com/dmlc/xgboost

What is claimed is:
 1. A method for monitoring an e-commerce platform, the method comprising: receiving, by a computing device, a feedback submitted by a user through the e-commerce platform; generating a vector based on content of the feedback, context of the feedback and profile of the user by feedback processors of the computing device, wherein the content comprises at least one of text, voice, image and video, wherein the context comprises at least one of time of submitting the feedback, location of submitting the feedback, and submission channel of the feedback, and wherein the user profile comprises at least one of attributes of the user, purchasing history of the user, and preference of the user using the e-commerce platform; and classifying, by classifiers of the computing device, the vector to obtain a function of the e-commerce platform corresponding to the feedback and a status of the function, and preparing an alarm when the status is malfunction, wherein the vector comprises a pre-determined number of dimensions, and each of the text, the voice, the image, the video, the time of submitting the feedback, the location of submitting the feedback, the submission channel of the feedback, the attributes of the user, the purchasing history of the user, and the preference of the user corresponds to at least one of the dimensions of the vector.
 2. The method of claim 1, wherein the feedback processors and the classifiers are performed using at least one artificial intelligence model.
 3. The method of claim 2, wherein the step of generating the vector comprises: processing the content using the feedback processors to obtain content dimensions of the vector corresponding to the text, the voice, the image, and the video.
 4. The method of claim 3, further comprising: cleaning the content before processing the content to obtain the content dimensions of the vector.
 5. The method of claim 3, further comprising: separating the image to text of the image and background image, processing the text of the image to obtain an image text result and processing the background image to obtain a background image result, and integrating the image text result and the background image result to obtain the content dimension of the vector corresponding to the image.
 6. The method of claim 2, further comprising: sending the alarm to a manager of the e-commerce platform responsible for the function, receiving an instruction corresponding to the alarm from the manager when the alarm is false, and re-train the feedback processors and the classifiers using the instruction.
 7. The method of claim 2, wherein the classifiers are trained using a plurality of historical feedbacks and a function category structure, the function category structure comprises: a tier-1 category comprising website of the e-commerce platform, application of the e-commerce platform, and external links to the e-commerce platform.
 8. The method of claim 7, wherein the tier-1 category of the website comprises tier-2 categories of: product page, shopping cart, and payment.
 9. The method of claim 8, wherein the tier-2 category of the product page comprises tier-3 categories of: product description, product search, and product recommendation.
 10. The method of claim 7, wherein the classifiers comprise a plurality of classification models, each classification model provides a candidate function based on each of the historical feedbacks, and the candidate functions provided by the classification models are used by an ensemble model to determine the function corresponding to each of the feedback.
 11. A system for monitoring an e-commerce platform, the system comprising a computing device, the computing device comprising a processor and a storage device storing computer executable code, wherein the computer executable code, when executed at the processor, is configured to: receive a feedback submitted by a user through the e-commerce platform; generate a vector based on content of the feedback, context of the feedback and profile of the user, wherein the content comprises at least one of text, voice, image and video, wherein the context comprises at least one of time of submitting the feedback, location of submitting the feedback, and submission channel of the feedback, and wherein the user profile comprises at least one of attributes of the user, purchasing history of the user, and preference of the user using the e-commerce platform; and classify the vector to obtain a function of the e-commerce platform corresponding to the feedback and a status of the function, and prepare an alarm when the status is malfunction, wherein the vector comprises a pre-determined number of dimensions, and each of the text, the voice, the image, the video, the time of submitting the feedback, the location of submitting the feedback, the submission channel of the feedback, the attributes of the user, the purchasing history of the user, and the preference of the user corresponds to at least one of the dimensions of the vector.
 12. The system of claim 11, wherein computer executable code comprises feedback processors to generate the vector and classifiers to classify the vector, and the feedback processors and the classifiers comprises artificial intelligence models.
 13. The system of claim 12, wherein the computer executable code is configured to generate the vector by: cleaning the content, and processing the content using the feedback processors to obtain content dimensions of the vector corresponding to the text, the voice, the image, and the video.
 14. The system of claim 13, wherein the computer executable code is further configured to: separate the image to text of the image and background image, process the text of the image to obtain an image text result and process the background image to obtain a background image result, and integrate the image text result and the background image result to obtain the content dimension of the vector corresponding to the image.
 15. The system of claim 12, wherein the computer executable code is further configured to: send the alarm to a manager of the e-commerce platform responsible for the function, receive an instruction corresponding to the alarm from the manager when the alarm is false, and re-train the feedback processors and the classifiers using the instruction.
 16. The system of claim 11, wherein the classifiers are trained using a plurality of historical feedbacks and a function category structure, the function category structure comprises: a tier-1 category comprising website of the e-commerce platform, application of the e-commerce platform, and external links to the e-commerce platform, the tier-1 category of the website comprises tier-2 categories of: product page, shopping cart, and payment, and the tier-2 category of the product page comprises tier-3 categories of: product description, product search, and product recommendation.
 17. The system of claim 16, wherein the classifiers comprise a plurality of classification models, each classification model provides a candidate function based on each of the historical feedbacks, and the candidate functions provided by the classification models are used by an ensemble model to determine the function corresponding to each of the feedback.
 18. A non-transitory computer readable medium storing computer executable code, wherein the computer executable code, when executed at a processor of a computing device, is configured to: receive a feedback submitted by a user through the e-commerce platform; generate a vector based on content of the feedback, context of the feedback and profile of the user, wherein the content comprises at least one of text, voice, image and video, wherein the context comprises at least one of time of submitting the feedback, location of submitting the feedback, and submission channel of the feedback, and wherein the user profile comprises at least one of attributes of the user, purchasing history of the user, and preference of the user using the e-commerce platform; and classify the vector to obtain a function of the e-commerce platform corresponding to the feedback and a status of the function, and prepare an alarm when the status is malfunction, wherein vector comprises a pre-determined number of dimensions, and each of the text, the voice, the image, the video, the time of submitting the feedback, the location of submitting the feedback, the submission channel of the feedback, the attributes of the user, the purchasing history of the user, and the preference of the user corresponds to at least one of the dimensions of the vector.
 19. The non-transitory computer readable medium of claim 18, wherein the computer executable code comprises feedback processors to generate the vector and classifiers to classify the vector, and the feedback processors and the classifiers comprises artificial intelligence models.
 20. The non-transitory computer readable medium of claim 18, wherein the computer executable code is configured to process the image by: separating the image to text of the image and background image, processing the text of the image to obtain an image text result, processing the background image to obtain a background image result, and integrating the image text result and the background image result to obtain the dimension of the vector corresponding to the image. 